Extracting value from product stewardship’s big data

Extracting value from product stewardship’s big data

Extracting value from product stewardship’s big data 150 150 Richard Fontaine

The term big data seems to be everywhere these days. A few examples of use cases for big data are fraud and crime detection, patient history in the healthcare industry, and social media analysis.

But what does it mean for product stewardship?

According to IBM, big data is defined as “data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety.”

To put it simply, the amount of data in the world is growing exponentially by the day and mere mortals like us struggle to keep up with the multiple concepts of data generated. It is estimated that by 2020, 1.7 megabytes will be created every second, for every person on earth.

The potential of big data is clear. But extracting value from big data is difficult, to say the least. As Nate Silver of FiveThirtyEight writes in his  book The Signal and the Noise, “The signal is the truth. The noise is what distracts us from the truth.” Unfortunately, big data creates a lot of noise. Product stewardship organizations understand this problem all too well.

Product stewardship organizations in the chemical, food and beverage, pharmaceutical, and other industries struggle to manage and analyze the vast data evaluating the potential human harm associated with the products they manufacture and sell.

Take the example of triclosan. Triclosan is a chemical with antimicrobial properties which destroy or inhibit the growth of microorganisms such as bacteria and fungi. Triclosan is commonly found in products like shaving cream, toothpaste, and bodywash. The FDA is closely monitoring Triclosan, but what are hundreds of scientists investigating triclosan throughout the world learning about associated human hazards like endocrine disruption and developmental and reproductivity toxicity? The ability to mine and model these big data can provide product stewardship organizations with a comprehensive, up-to-date, and unbiased view of these risks enabling nimble, better-informed business decision-making.

At Praedicat, we’re combining artificial intelligence with subject matter expertise to make sense of product stewardship’s big data. Interested in learning more? Please take a look at the recording of the recent ChemMeta product launch webinar.